Search: “evaluation”

Working Paper: Towards Schema-based Learning from a Category-Theoretic Perspective

arXiv:2604.10589v1 Announce Type: new Abstract: We introduce a hierarchical categorical framework for Schema-Based Learning (SBL) structured across four interconnected levels. At the schema level, a f

14 Apr 2026

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3D-VCD: Hallucination Mitigation in 3D-LLM Embodied Agents through Visual Contrastive Decoding

DGX agent

arXiv:2604.08645v1 Announce Type: cross Abstract: Large multimodal models are increasingly used as the reasoning core of embodied agents operating in 3D environments, yet they remain prone to hallucin

researcharxiv-cs-ai

13 Apr 2026

A fast and Generic Energy-Shifting Transformer for Hybrid Monte Carlo Radiotherapy Calculation

arXiv:2604.09157v1 Announce Type: cross Abstract: We introduce a novel learning framework for accelerated Monte Carlo (MC) dose calculation termed Energy-Shifting. This approach leverages deep learnin

A Semi-Automated Framework for 3D Reconstruction of Medieval Manuscript Miniatures

arXiv:2604.08610v1 Announce Type: new Abstract: This paper presents a semi-automated framework for transforming two-dimensional miniatures from medieval manuscripts into three-dimensional digital mode

Accelerating Transformer-Based Monocular SLAM via Geometric Utility Scoring

arXiv:2604.08718v1 Announce Type: cross Abstract: Geometric Foundation Models (GFMs) have recently advanced monocular SLAM by providing robust, calibration-free 3D priors. However, deploying these mod

researcharxiv-cs-ai

ActionNex: A Virtual Outage Manager for Cloud Computing

arXiv:2604.03512v2 Announce Type: replace Abstract: Outage management in large-scale cloud operations remains heavily manual, requiring rapid triage, cross-team coordination, and experience-driven dec

AdaCubic: An Adaptive Cubic Regularization Optimizer for Deep Learning

arXiv:2604.09437v1 Announce Type: new Abstract: A novel regularization technique, AdaCubic, is proposed that adapts the weight of the cubic term. The heart of AdaCubic is an auxiliary optimization pro

Adaptive Tuning of Parameterized Traffic Controllers via Multi-Agent Reinforcement Learning

model-releasesarxiv-cs-lg

arXiv:2512.07417v2 Announce Type: replace Abstract: Effective traffic control is essential for mitigating congestion in transportation networks. Conventional traffic management strategies, including r

Adding Another Dimension to Image-based Animal Detection

arXiv:2604.09210v1 Announce Type: new Abstract: Monocular imaging of animals inherently reduces 3D structures to 2D projections. Detection algorithms lead to 2D bounding boxes that lack information ab

AlphaCast: A Human Wisdom-LLM Intelligence Co-Reasoning Framework for Interactive Time Series Forecasting

arXiv:2511.08947v5 Announce Type: replace Abstract: Time series forecasting plays a crucial role in decision-making across many real-world applications. Despite substantial progress, most existing met

ALTO: Adaptive LoRA Tuning and Orchestration for Heterogeneous LoRA Training Workloads

arXiv:2604.05426v2 Announce Type: replace-cross Abstract: Low-Rank Adaptation (LoRA) is now the dominant method for parameter-efficient fine-tuning of large language models, but achieving a high-quali

AMO-ENE: Attention-based Multi-Omics Fusion Model for Outcome Prediction in Extra Nodal Extension and HPV-associated Oropharyngeal Cancer

arXiv:2604.09280v1 Announce Type: cross Abstract: Extranodal extension (ENE) is an emerging prognostic factor in human papillomavirus (HPV)-associated oropharyngeal cancer (OPC), although it is curren

An Adaptive Model Selection Framework for Demand Forecasting under Horizon-Induced Degradation to Support Business Strategy and Operations

arXiv:2602.13939v3 Announce Type: replace-cross Abstract: Business environments characterized by intermittent demand, high variability, and multi-step planning horizons require forecasting policies th

Bayesian Social Deduction with Graph-Informed Language Models

arXiv:2506.17788v2 Announce Type: replace Abstract: Social reasoning - inferring unobservable beliefs and intentions from partial observations of other agents - remains a challenging task for large la

Beyond Relevance: Utility-Centric Retrieval in the LLM Era

arXiv:2604.08920v1 Announce Type: cross Abstract: Information retrieval systems have traditionally optimized for topical relevance-the degree to which retrieved documents match a query. However, relev

Bharat Scene Text: A Novel Comprehensive Dataset and Benchmark for Indian Language Scene Text Understanding

arXiv:2511.23071v2 Announce Type: replace-cross Abstract: Reading scene text, that is, text appearing in images, has numerous application areas, including assistive technology, search, and e-commerce.

Bringing Clustering to MLL: Weakly-Supervised Clustering for Partial Multi-Label Learning

arXiv:2604.09359v1 Announce Type: new Abstract: Label noise in multi-label learning (MLL) poses significant challenges for model training, particularly in partial multi-label learning (PML) where cand

CAD 100K: A Comprehensive Multi-Task Dataset for Car Related Visual Anomaly Detection

model-releasesarxiv-cs-cv

arXiv:2604.09023v1 Announce Type: new Abstract: Multi-task visual anomaly detection is critical for car-related manufacturing quality assessment. However, existing methods remain task-specific, hinder

Local Ai

Case-Grounded Evidence Verification: A Framework for Constructing Evidence-Sensitive Supervision

arXiv:2604.09537v1 Announce Type: cross Abstract: Evidence-grounded reasoning requires more than attaching retrieved text to a prediction: a model should make decisions that depend on whether the prov

local-aiarxiv-cs-ai

CatalogStitch: Dimension-Aware and Occlusion-Preserving Object Compositing for Catalog Image Generation

model-releasesarxiv-cs-cv

arXiv:2604.08836v1 Announce Type: new Abstract: Generative object compositing methods have shown remarkable ability to seamlessly insert objects into scenes. However, when applied to real-world catalo

Chain-in-Tree: Back to Sequential Reasoning in LLM Tree Search

arXiv:2509.25835v4 Announce Type: replace Abstract: Test-time scaling improves large language models (LLMs) on long-horizon reasoning tasks by allocating more compute at inference. LLM inference via t

ChipSeek: Optimizing Verilog Generation via EDA-Integrated Reinforcement Learning

arXiv:2507.04736v2 Announce Type: replace Abstract: Large Language Models have emerged as powerful tools for automating Register-Transfer Level (RTL) code generation, yet they face critical limitation

Chronological Contrastive Learning: Few-Shot Progression Assessment in Irreversible Diseases

arXiv:2603.21935v2 Announce Type: replace-cross Abstract: Quantitative disease severity scoring in medical imaging is costly, time-consuming, and subject to inter-reader variability. At the same time,

researcharxiv-cs-ai

CodeScout: Contextual Problem Statement Enhancement for Software Agents

arXiv:2603.05744v2 Announce Type: replace Abstract: Current AI-powered code assistance tools often struggle with poorly-defined problem statements that lack sufficient task context and requirements sp

agentsarxiv-cs-cl

Commanding Humanoid by Free-form Language: A Large Language Action Model with Unified Motion Vocabulary

arXiv:2511.22963v2 Announce Type: replace-cross Abstract: Enabling humanoid robots to follow free-form language commands is critical for seamless human-robot interaction, collaborative task execution,

Applications

CoMoVi: Co-Generation of 3D Human Motions and Realistic Videos

arXiv:2601.10632v2 Announce Type: replace Abstract: In this paper, we find that the generation of 3D human motions and 2D human videos is intrinsically coupled. 3D motions provide the structural prior

applicationsarxiv-cs-cv

Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity

model-releasesarxiv-cs-lg

arXiv:2501.19038v3 Announce Type: replace-cross Abstract: Conformal prediction has emerged as a widely used framework for constructing valid prediction sets in classification and regression tasks. In

Constraining Sequential Model Editing with Editing Anchor Compression

arXiv:2503.00035v2 Announce Type: replace-cross Abstract: Large language models (LLMs) struggle with hallucinations due to false or outdated knowledge. Given the high resource demands of retraining th

CORA: Conformal Risk-Controlled Agents for Safeguarded Mobile GUI Automation

arXiv:2604.09155v1 Announce Type: cross Abstract: Graphical user interface (GUI) agents powered by vision language models (VLMs) are rapidly moving from passive assistance to autonomous operation. How

Cross-Lingual Attention Distillation with Personality-Informed Generative Augmentation for Multilingual Personality Recognition

model-releasesarxiv-cs-cl

arXiv:2604.08851v1 Announce Type: new Abstract: While significant work has been done on personality recognition, the lack of multilingual datasets remains an unresolved challenge. To address this, we

Detecting Diffusion-generated Images via Dynamic Assembly ForestsDetecting Diffusion-generated Images via Dynamic Assembly Forests

arXiv:2604.09106v1 Announce Type: new Abstract: Diffusion models are known for generating high-quality images, causing serious security concerns. To combat this, most efforts rely on deep neural netwo

Detection of Hate and Threat in Digital Forensics: A Case-Driven Multimodal Approach

arXiv:2604.08609v1 Announce Type: cross Abstract: Digital forensic investigations increasingly rely on heterogeneous evidence such as images, scanned documents, and contextual reports. These artifacts

researcharxiv-cs-ai

Applications

eBandit: Kernel-Driven Reinforcement Learning for Adaptive Video Streaming

arXiv:2604.08791v1 Announce Type: cross Abstract: User-space Adaptive Bitrate (ABR) algorithms cannot see the transport layer signals that matter most, such as minimum RTT and instantaneous delivery r

applicationsarxiv-cs-ai

Efficient Unlearning through Maximizing Relearning Convergence Delay

arXiv:2604.09391v1 Announce Type: cross Abstract: Machine unlearning poses challenges in removing mislabeled, contaminated, or problematic data from a pretrained model. Current unlearning approaches a

Envisioning the Future, One Step at a Time

arXiv:2604.09527v1 Announce Type: cross Abstract: Accurately anticipating how complex, diverse scenes will evolve requires models that represent uncertainty, simulate along extended interaction chains

Applications

Evidential Transformation Network: Turning Pretrained Models into Evidential Models for Post-hoc Uncertainty Estimation

arXiv:2604.08627v1 Announce Type: cross Abstract: Pretrained models have become standard in both vision and language, yet they typically do not provide reliable measures of confidence. Existing uncert

applicationsarxiv-cs-ai

Evolutionary Optimization Trumps Adam Optimization on Embedding Space Exploration

arXiv:2511.03913v2 Announce Type: replace-cross Abstract: Deep diffusion models have revolutionized image generation by producing high-quality outputs. However, achieving specific objectives with thes

Exploiting Web Search Tools of AI Agents for Data Exfiltration

arXiv:2510.09093v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are now routinely used to autonomously execute complex tasks, from natural language processing to dynamic workflo

researcharxiv-cs-cl

Few-Shot Contrastive Adaptation for Audio Abuse Detection in Low-Resource Indic Languages

arXiv:2604.09094v1 Announce Type: cross Abstract: Abusive speech detection is becoming increasingly important as social media shifts towards voice-based interaction, particularly in multilingual and l

researcharxiv-cs-cl

FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarsening

model-releasesarxiv-cs-lg

arXiv:2410.15001v5 Announce Type: replace Abstract: Scalability of Graph Neural Networks (GNNs) remains a significant challenge. To tackle this, methods like coarsening, condensation, and computation

Generative Simulation for Policy Learning in Physical Human-Robot Interaction

arXiv:2604.08664v1 Announce Type: new Abstract: Developing autonomous physical human-robot interaction (pHRI) systems is limited by the scarcity of large-scale training data to learn robust robot beha

safetyarxiv-cs-ro

Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar Models for Authorship Verification

arXiv:2403.08462v3 Announce Type: replace Abstract: Authorship Verification (AV) is a key area of research in digital text forensics, which addresses the fundamental question of whether two texts were

researcharxiv-cs-cl

Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning

arXiv:2404.10976v4 Announce Type: replace-cross Abstract: Cooperative Multi-Agent Reinforcement Learning (MARL) necessitates seamless collaboration among agents, often represented by an underlying rel

H-AdminSim: A Multi-Agent Simulator for Realistic Hospital Administrative Workflows with FHIR Integration

arXiv:2602.05407v2 Announce Type: replace Abstract: Hospital administration departments handle a wide range of operational tasks and, in large hospitals, process over 10,000 requests per day, driving

Hierarchical Alignment: Enforcing Hierarchical Instruction-Following in LLMs through Logical Consistency

arXiv:2604.09075v1 Announce Type: new Abstract: Large language models increasingly operate under multiple instructions from heterogeneous sources with different authority levels, including system poli

safetyarxiv-cs-cl

Hierarchical Flow Decomposition for Turning Movement Prediction at Signalized Intersections

arXiv:2604.09336v1 Announce Type: new Abstract: Accurate prediction of intersection turning movements is essential for adaptive signal control but remains difficult due to the high volatility of direc

HiFloat4 Format for Language Model Pre-training on Ascend NPUs

arXiv:2604.08826v1 Announce Type: cross Abstract: Large foundation models have become central to modern machine learning, with performance scaling predictably with model size and data. However, traini

IKKA: Inversion Classification via Critical Anomalies for Robust Visual Servoing

arXiv:2604.08754v1 Announce Type: new Abstract: We introduce IKKA (Inversion Classification via Critical Anomalies), a topologically motivated weighting framework for robust visual servoing under dist